Overview

Brought to you by YData

Dataset statistics

Number of variables36
Number of observations121856
Missing cells0
Missing cells (%)0.0%
Duplicate rows4615
Duplicate rows (%)3.8%
Total size in memory92.9 MiB
Average record size in memory799.8 B

Variable types

Numeric14
Categorical19
Boolean2
Text1

Alerts

Mobile_Tag has constant value "1" Constant
Homephone_Tag has constant value "0" Constant
Cleint_City_Rating has constant value "2.0" Constant
Dataset has 4615 (3.8%) duplicate rowsDuplicates
Age_Days is highly overall correlated with Employed_DaysHigh correlation
Child_Count is highly overall correlated with Client_Family_MembersHigh correlation
Client_Family_Members is highly overall correlated with Child_Count and 1 other fieldsHigh correlation
Client_Marital_Status is highly overall correlated with Client_Family_MembersHigh correlation
Credit_Amount is highly overall correlated with Loan_AnnuityHigh correlation
Employed_Days is highly overall correlated with Age_DaysHigh correlation
Loan_Annuity is highly overall correlated with Credit_AmountHigh correlation
Accompany_Client is highly imbalanced (66.6%) Imbalance
Client_Education is highly imbalanced (53.6%) Imbalance
Loan_Contract_Type is highly imbalanced (56.0%) Imbalance
Client_Housing_Type is highly imbalanced (72.8%) Imbalance
Client_Permanent_Match_Tag is highly imbalanced (60.8%) Imbalance
Default is highly imbalanced (59.5%) Imbalance
Application_Process_Day has 6287 (5.2%) zeros Zeros
Phone_Change has 14555 (11.9%) zeros Zeros
Credit_Bureau has 28003 (23.0%) zeros Zeros

Reproduction

Analysis started2025-06-22 13:40:58.156705
Analysis finished2025-06-22 13:41:28.629384
Duration30.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Client_Income
Real number (ℝ)

Distinct1050
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16205.608
Minimum2565
Maximum33750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:28.685433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2565
5-th percentile6750
Q111250
median14400
Q320250
95-th percentile33300
Maximum33750
Range31185
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation7233.5882
Coefficient of variation (CV)0.44636327
Kurtosis0.067236525
Mean16205.608
Median Absolute Deviation (MAD)4050
Skewness0.8165309
Sum1.9747506 × 109
Variance52324798
MonotonicityNot monotonic
2025-06-22T19:11:28.772321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13500 13717
 
11.3%
11250 11940
 
9.8%
15750 10146
 
8.3%
18000 9514
 
7.8%
9000 8705
 
7.1%
22500 7975
 
6.5%
20250 6309
 
5.2%
33750 6086
 
5.0%
14400 4833
 
4.0%
6750 4190
 
3.4%
Other values (1040) 38441
31.5%
ValueCountFrequency (%)
2565 1
 
< 0.1%
2610 1
 
< 0.1%
2646 1
 
< 0.1%
2700 25
< 0.1%
2790 5
 
< 0.1%
2835 3
 
< 0.1%
2840.4 1
 
< 0.1%
2857.5 2
 
< 0.1%
2872.35 1
 
< 0.1%
2880 3
 
< 0.1%
ValueCountFrequency (%)
33750 6086
5.0%
33300 29
 
< 0.1%
32850 38
 
< 0.1%
32731.65 1
 
< 0.1%
32625 1
 
< 0.1%
32580 1
 
< 0.1%
32480.55 1
 
< 0.1%
32400 61
 
0.1%
32396.85 1
 
< 0.1%
32265 1
 
< 0.1%

Car_Owned
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0.0
81305 
1.0
40551 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 81305
66.7%
1.0 40551
33.3%

Length

2025-06-22T19:11:28.844615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:28.897949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 81305
66.7%
1.0 40551
33.3%

Most occurring characters

ValueCountFrequency (%)
0 203161
55.6%
. 121856
33.3%
1 40551
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 203161
55.6%
. 121856
33.3%
1 40551
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 203161
55.6%
. 121856
33.3%
1 40551
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 203161
55.6%
. 121856
33.3%
1 40551
 
11.1%

Bike_Owned
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0.0
82572 
1.0
39284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 82572
67.8%
1.0 39284
32.2%

Length

2025-06-22T19:11:28.955813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:29.003775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 82572
67.8%
1.0 39284
32.2%

Most occurring characters

ValueCountFrequency (%)
0 204428
55.9%
. 121856
33.3%
1 39284
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 204428
55.9%
. 121856
33.3%
1 39284
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 204428
55.9%
. 121856
33.3%
1 39284
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 204428
55.9%
. 121856
33.3%
1 39284
 
10.7%

Active_Loan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0.0
62843 
1.0
59013 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 62843
51.6%
1.0 59013
48.4%

Length

2025-06-22T19:11:29.052987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:29.096003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 62843
51.6%
1.0 59013
48.4%

Most occurring characters

ValueCountFrequency (%)
0 184699
50.5%
. 121856
33.3%
1 59013
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 184699
50.5%
. 121856
33.3%
1 59013
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 184699
50.5%
. 121856
33.3%
1 59013
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 184699
50.5%
. 121856
33.3%
1 59013
 
16.1%

House_Own
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
1.0
85459 
0.0
36397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 85459
70.1%
0.0 36397
29.9%

Length

2025-06-22T19:11:29.143203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:29.184794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 85459
70.1%
0.0 36397
29.9%

Most occurring characters

ValueCountFrequency (%)
0 158253
43.3%
. 121856
33.3%
1 85459
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 158253
43.3%
. 121856
33.3%
1 85459
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 158253
43.3%
. 121856
33.3%
1 85459
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 158253
43.3%
. 121856
33.3%
1 85459
23.4%

Child_Count
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
0.0
86472 
1.0
23431 
2.0
10294 
2.5
 
1659

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 86472
71.0%
1.0 23431
 
19.2%
2.0 10294
 
8.4%
2.5 1659
 
1.4%

Length

2025-06-22T19:11:29.234177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:29.275189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 86472
71.0%
1.0 23431
 
19.2%
2.0 10294
 
8.4%
2.5 1659
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 206669
56.5%
. 121856
33.3%
1 23431
 
6.4%
2 11953
 
3.3%
5 1659
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206669
56.5%
. 121856
33.3%
1 23431
 
6.4%
2 11953
 
3.3%
5 1659
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206669
56.5%
. 121856
33.3%
1 23431
 
6.4%
2 11953
 
3.3%
5 1659
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206669
56.5%
. 121856
33.3%
1 23431
 
6.4%
2 11953
 
3.3%
5 1659
 
0.5%

Credit_Amount
Real number (ℝ)

High correlation 

Distinct3794
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59125.546
Minimum4500
Maximum160987.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:29.344764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4500
5-th percentile14220
Q127450
median51750
Q380865
95-th percentile135000
Maximum160987.5
Range156487.5
Interquartile range (IQR)53415

Descriptive statistics

Standard deviation37532.095
Coefficient of variation (CV)0.63478644
Kurtosis0.15338426
Mean59125.546
Median Absolute Deviation (MAD)24750
Skewness0.90531368
Sum7.2048025 × 109
Variance1.4086581 × 109
MonotonicityNot monotonic
2025-06-22T19:11:29.427906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51750 3744
 
3.1%
45000 3733
 
3.1%
67500 3450
 
2.8%
22500 3127
 
2.6%
18000 2761
 
2.3%
27000 2748
 
2.3%
160987.5 2599
 
2.1%
90000 2366
 
1.9%
25470 1759
 
1.4%
54504 1726
 
1.4%
Other values (3784) 93843
77.0%
ValueCountFrequency (%)
4500 76
0.1%
4797 77
0.1%
4945.5 10
 
< 0.1%
4950 13
 
< 0.1%
4975.2 23
 
< 0.1%
5094 160
0.1%
5212.8 30
 
< 0.1%
5276.7 10
 
< 0.1%
5391 22
 
< 0.1%
5400 22
 
< 0.1%
ValueCountFrequency (%)
160987.5 2599
2.1%
160927.2 5
 
< 0.1%
160786.8 1
 
< 0.1%
160741.8 1
 
< 0.1%
160603.65 2
 
< 0.1%
160531.2 2
 
< 0.1%
160200 3
 
< 0.1%
160100.1 3
 
< 0.1%
160033.05 4
 
< 0.1%
159777.9 1
 
< 0.1%

Loan_Annuity
Real number (ℝ)

High correlation 

Distinct9743
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2676.7858
Minimum217.35
Maximum5988.6562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:29.511232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum217.35
5-th percentile900
Q11687.5
median2499.75
Q33407.9625
95-th percentile5324.85
Maximum5988.6562
Range5771.3062
Interquartile range (IQR)1720.4625

Descriptive statistics

Standard deviation1293.9121
Coefficient of variation (CV)0.48338274
Kurtosis-0.016443914
Mean2676.7858
Median Absolute Deviation (MAD)841.05
Skewness0.69186522
Sum3.2618242 × 108
Variance1674208.5
MonotonicityNot monotonic
2025-06-22T19:11:29.595038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2499.75 4857
 
4.0%
5988.65625 3220
 
2.6%
900 2370
 
1.9%
1350 2091
 
1.7%
675 845
 
0.7%
1012.5 774
 
0.6%
3780 621
 
0.5%
2621.7 570
 
0.5%
1125 566
 
0.5%
1237.5 487
 
0.4%
Other values (9733) 105455
86.5%
ValueCountFrequency (%)
217.35 2
< 0.1%
218.7 2
< 0.1%
229.5 1
< 0.1%
241.2 2
< 0.1%
258.3 1
< 0.1%
259.65 1
< 0.1%
260.55 2
< 0.1%
274.95 1
< 0.1%
275.4 2
< 0.1%
284.4 1
< 0.1%
ValueCountFrequency (%)
5988.65625 3220
2.6%
5988.15 3
 
< 0.1%
5985.45 2
 
< 0.1%
5985 2
 
< 0.1%
5984.55 1
 
< 0.1%
5982.3 1
 
< 0.1%
5980.5 1
 
< 0.1%
5978.7 3
 
< 0.1%
5977.8 1
 
< 0.1%
5977.35 4
 
< 0.1%

Accompany_Client
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
Alone
99155 
Relative
15748 
Partner
 
4516
Kids
 
1334
Others
 
987
Other values (2)
 
116

Length

Max length8
Median length5
Mean length5.4586807
Min length2

Characters and Unicode

Total characters665173
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowAlone
3rd rowAlone
4th rowAlone
5th rowAlone

Common Values

ValueCountFrequency (%)
Alone 99155
81.4%
Relative 15748
 
12.9%
Partner 4516
 
3.7%
Kids 1334
 
1.1%
Others 987
 
0.8%
Group 104
 
0.1%
## 12
 
< 0.1%

Length

2025-06-22T19:11:29.678102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:29.771173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
alone 99155
81.4%
relative 15748
 
12.9%
partner 4516
 
3.7%
kids 1334
 
1.1%
others 987
 
0.8%
group 104
 
0.1%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 136154
20.5%
l 114903
17.3%
n 103671
15.6%
o 99259
14.9%
A 99155
14.9%
t 21251
 
3.2%
a 20264
 
3.0%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 665173
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 136154
20.5%
l 114903
17.3%
n 103671
15.6%
o 99259
14.9%
A 99155
14.9%
t 21251
 
3.2%
a 20264
 
3.0%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 665173
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 136154
20.5%
l 114903
17.3%
n 103671
15.6%
o 99259
14.9%
A 99155
14.9%
t 21251
 
3.2%
a 20264
 
3.0%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 665173
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 136154
20.5%
l 114903
17.3%
n 103671
15.6%
o 99259
14.9%
A 99155
14.9%
t 21251
 
3.2%
a 20264
 
3.0%
i 17082
 
2.6%
R 15748
 
2.4%
v 15748
 
2.4%
Other values (11) 21938
 
3.3%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Service
64729 
Commercial
27764 
Retired
21043 
Govt Job
8303 
Student
 
8
Other values (3)
 
9

Length

Max length15
Median length7
Mean length7.7519777
Min length7

Characters and Unicode

Total characters944625
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCommercial
2nd rowService
3rd rowService
4th rowRetired
5th rowCommercial

Common Values

ValueCountFrequency (%)
Service 64729
53.1%
Commercial 27764
22.8%
Retired 21043
 
17.3%
Govt Job 8303
 
6.8%
Student 8
 
< 0.1%
Unemployed 6
 
< 0.1%
Maternity leave 2
 
< 0.1%
Businessman 1
 
< 0.1%

Length

2025-06-22T19:11:30.378479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.437547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
service 64729
49.7%
commercial 27764
21.3%
retired 21043
 
16.2%
govt 8303
 
6.4%
job 8303
 
6.4%
student 8
 
< 0.1%
unemployed 6
 
< 0.1%
maternity 2
 
< 0.1%
leave 2
 
< 0.1%
businessman 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 199335
21.1%
i 113539
12.0%
r 113538
12.0%
c 92493
9.8%
v 73034
 
7.7%
S 64737
 
6.9%
m 55535
 
5.9%
o 44376
 
4.7%
t 29366
 
3.1%
l 27772
 
2.9%
Other values (16) 130900
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 944625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 199335
21.1%
i 113539
12.0%
r 113538
12.0%
c 92493
9.8%
v 73034
 
7.7%
S 64737
 
6.9%
m 55535
 
5.9%
o 44376
 
4.7%
t 29366
 
3.1%
l 27772
 
2.9%
Other values (16) 130900
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 944625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 199335
21.1%
i 113539
12.0%
r 113538
12.0%
c 92493
9.8%
v 73034
 
7.7%
S 64737
 
6.9%
m 55535
 
5.9%
o 44376
 
4.7%
t 29366
 
3.1%
l 27772
 
2.9%
Other values (16) 130900
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 944625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 199335
21.1%
i 113539
12.0%
r 113538
12.0%
c 92493
9.8%
v 73034
 
7.7%
S 64737
 
6.9%
m 55535
 
5.9%
o 44376
 
4.7%
t 29366
 
3.1%
l 27772
 
2.9%
Other values (16) 130900
13.9%

Client_Education
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
Secondary
87556 
Graduation
28819 
Graduation dropout
 
3960
Junior secondary
 
1455
Post Grad
 
66

Length

Max length18
Median length9
Mean length9.6125591
Min length9

Characters and Unicode

Total characters1171348
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary
2nd rowGraduation
3rd rowGraduation dropout
4th rowSecondary
5th rowSecondary

Common Values

ValueCountFrequency (%)
Secondary 87556
71.9%
Graduation 28819
 
23.7%
Graduation dropout 3960
 
3.2%
Junior secondary 1455
 
1.2%
Post Grad 66
 
0.1%

Length

2025-06-22T19:11:30.510962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.559505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
secondary 89011
69.9%
graduation 32779
 
25.7%
dropout 3960
 
3.1%
junior 1455
 
1.1%
post 66
 
0.1%
grad 66
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 154635
13.2%
o 131231
11.2%
r 127271
10.9%
d 125816
10.7%
n 123245
10.5%
e 89011
7.6%
y 89011
7.6%
c 89011
7.6%
S 87556
7.5%
u 38194
 
3.3%
Other values (8) 116367
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1171348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 154635
13.2%
o 131231
11.2%
r 127271
10.9%
d 125816
10.7%
n 123245
10.5%
e 89011
7.6%
y 89011
7.6%
c 89011
7.6%
S 87556
7.5%
u 38194
 
3.3%
Other values (8) 116367
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1171348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 154635
13.2%
o 131231
11.2%
r 127271
10.9%
d 125816
10.7%
n 123245
10.5%
e 89011
7.6%
y 89011
7.6%
c 89011
7.6%
S 87556
7.5%
u 38194
 
3.3%
Other values (8) 116367
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1171348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 154635
13.2%
o 131231
11.2%
r 127271
10.9%
d 125816
10.7%
n 123245
10.5%
e 89011
7.6%
y 89011
7.6%
c 89011
7.6%
S 87556
7.5%
u 38194
 
3.3%
Other values (8) 116367
9.9%

Client_Marital_Status
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
M
90822 
S
17404 
D
 
7556
W
 
6074

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowW
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 90822
74.5%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%

Length

2025-06-22T19:11:30.621607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.663632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 90822
74.5%
s 17404
 
14.3%
d 7556
 
6.2%
w 6074
 
5.0%

Most occurring characters

ValueCountFrequency (%)
M 90822
74.5%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 90822
74.5%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 90822
74.5%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 90822
74.5%
S 17404
 
14.3%
D 7556
 
6.2%
W 6074
 
5.0%

Client_Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Male
80876 
Female
40977 
XNA
 
3

Length

Max length6
Median length4
Mean length4.6725233
Min length3

Characters and Unicode

Total characters569375
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 80876
66.4%
Female 40977
33.6%
XNA 3
 
< 0.1%

Length

2025-06-22T19:11:30.712454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.754050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 80876
66.4%
female 40977
33.6%
xna 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 162830
28.6%
l 121853
21.4%
a 121853
21.4%
M 80876
14.2%
F 40977
 
7.2%
m 40977
 
7.2%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 569375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 162830
28.6%
l 121853
21.4%
a 121853
21.4%
M 80876
14.2%
F 40977
 
7.2%
m 40977
 
7.2%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 569375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 162830
28.6%
l 121853
21.4%
a 121853
21.4%
M 80876
14.2%
F 40977
 
7.2%
m 40977
 
7.2%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 569375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 162830
28.6%
l 121853
21.4%
a 121853
21.4%
M 80876
14.2%
F 40977
 
7.2%
m 40977
 
7.2%
X 3
 
< 0.1%
N 3
 
< 0.1%
A 3
 
< 0.1%

Loan_Contract_Type
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
CL
110769 
RL
11087 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters243712
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCL
2nd rowCL
3rd rowCL
4th rowCL
5th rowCL

Common Values

ValueCountFrequency (%)
CL 110769
90.9%
RL 11087
 
9.1%

Length

2025-06-22T19:11:30.802101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.839179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cl 110769
90.9%
rl 11087
 
9.1%

Most occurring characters

ValueCountFrequency (%)
L 121856
50.0%
C 110769
45.5%
R 11087
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 243712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 121856
50.0%
C 110769
45.5%
R 11087
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 243712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 121856
50.0%
C 110769
45.5%
R 11087
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 243712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 121856
50.0%
C 110769
45.5%
R 11087
 
4.5%

Client_Housing_Type
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Home
108557 
Family
 
5783
Municipal
 
4248
Rental
 
1816
Office
 
1002

Length

Max length9
Median length4
Mean length4.3228565
Min length4

Characters and Unicode

Total characters526766
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowFamily
4th rowHome
5th rowHome

Common Values

ValueCountFrequency (%)
Home 108557
89.1%
Family 5783
 
4.7%
Municipal 4248
 
3.5%
Rental 1816
 
1.5%
Office 1002
 
0.8%
Shared 450
 
0.4%

Length

2025-06-22T19:11:30.885725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:30.927673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
home 108557
89.1%
family 5783
 
4.7%
municipal 4248
 
3.5%
rental 1816
 
1.5%
office 1002
 
0.8%
shared 450
 
0.4%

Most occurring characters

ValueCountFrequency (%)
m 114340
21.7%
e 111825
21.2%
o 108557
20.6%
H 108557
20.6%
i 15281
 
2.9%
a 12297
 
2.3%
l 11847
 
2.2%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 526766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 114340
21.7%
e 111825
21.2%
o 108557
20.6%
H 108557
20.6%
i 15281
 
2.9%
a 12297
 
2.3%
l 11847
 
2.2%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 526766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 114340
21.7%
e 111825
21.2%
o 108557
20.6%
H 108557
20.6%
i 15281
 
2.9%
a 12297
 
2.3%
l 11847
 
2.2%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 526766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 114340
21.7%
e 111825
21.2%
o 108557
20.6%
H 108557
20.6%
i 15281
 
2.9%
a 12297
 
2.3%
l 11847
 
2.2%
n 6064
 
1.2%
F 5783
 
1.1%
y 5783
 
1.1%
Other values (12) 26432
 
5.0%
Distinct80
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020227247
Minimum0.000533
Maximum0.050932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:31.001906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.000533
5-th percentile0.005002
Q10.010032
median0.01885
Q30.026392
95-th percentile0.04622
Maximum0.050932
Range0.050399
Interquartile range (IQR)0.01636

Descriptive statistics

Standard deviation0.011752793
Coefficient of variation (CV)0.58103773
Kurtosis0.042213531
Mean0.020227247
Median Absolute Deviation (MAD)0.008818
Skewness0.71691018
Sum2464.8114
Variance0.00013812815
MonotonicityNot monotonic
2025-06-22T19:11:31.076181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01885 7813
 
6.4%
0.035792 6267
 
5.1%
0.04622 5117
 
4.2%
0.030755 4639
 
3.8%
0.025164 4516
 
3.7%
0.026392 4330
 
3.6%
0.031329 4257
 
3.5%
0.028663 4227
 
3.5%
0.019101 3314
 
2.7%
0.050932 3305
 
2.7%
Other values (70) 74071
60.8%
ValueCountFrequency (%)
0.000533 17
 
< 0.1%
0.000938 12
 
< 0.1%
0.001276 194
 
0.2%
0.001333 94
 
0.1%
0.001417 189
 
0.2%
0.002042 587
0.5%
0.002134 395
0.3%
0.002506 365
0.3%
0.003069 671
0.6%
0.003122 444
0.4%
ValueCountFrequency (%)
0.050932 3305
2.7%
0.04622 5117
4.2%
0.035792 6267
5.1%
0.032561 2543
2.1%
0.031329 4257
3.5%
0.030755 4639
3.8%
0.028663 4227
3.5%
0.026392 4330
3.6%
0.025164 4516
3.7%
0.02461 2437
 
2.0%

Age_Days
Real number (ℝ)

High correlation 

Distinct17000
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16018.713
Minimum7676
Maximum25201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:31.151774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7676
5-th percentile9450
Q112512
median15734
Q319544
95-th percentile23181
Maximum25201
Range17525
Interquartile range (IQR)7032

Descriptive statistics

Standard deviation4301.3537
Coefficient of variation (CV)0.26852055
Kurtosis-0.98598876
Mean16018.713
Median Absolute Deviation (MAD)3505
Skewness0.12978872
Sum1.9519763 × 109
Variance18501644
MonotonicityNot monotonic
2025-06-22T19:11:31.226191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15734 3630
 
3.0%
10936 22
 
< 0.1%
20193 21
 
< 0.1%
13327 21
 
< 0.1%
13231 21
 
< 0.1%
12334 21
 
< 0.1%
14258 20
 
< 0.1%
13436 20
 
< 0.1%
20972 20
 
< 0.1%
15201 20
 
< 0.1%
Other values (16990) 118040
96.9%
ValueCountFrequency (%)
7676 2
< 0.1%
7678 1
 
< 0.1%
7679 1
 
< 0.1%
7680 3
< 0.1%
7683 1
 
< 0.1%
7687 1
 
< 0.1%
7688 2
< 0.1%
7689 1
 
< 0.1%
7690 2
< 0.1%
7691 2
< 0.1%
ValueCountFrequency (%)
25201 1
 
< 0.1%
25200 1
 
< 0.1%
25197 2
< 0.1%
25196 3
< 0.1%
25195 1
 
< 0.1%
25192 1
 
< 0.1%
25191 1
 
< 0.1%
25186 1
 
< 0.1%
25184 1
 
< 0.1%
25182 1
 
< 0.1%

Employed_Days
Real number (ℝ)

High correlation 

Distinct9430
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4036.7851
Minimum0
Maximum12019.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:31.302016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile232
Q1962
median2212
Q35385
95-th percentile12019.5
Maximum12019.5
Range12019.5
Interquartile range (IQR)4423

Descriptive statistics

Standard deviation4184.7239
Coefficient of variation (CV)1.0366477
Kurtosis-0.36264673
Mean4036.7851
Median Absolute Deviation (MAD)1539
Skewness1.1019092
Sum4.9190648 × 108
Variance17511914
MonotonicityNot monotonic
2025-06-22T19:11:31.375903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12019.5 21760
 
17.9%
2212 3680
 
3.0%
381 69
 
0.1%
212 66
 
0.1%
230 64
 
0.1%
231 61
 
0.1%
199 60
 
< 0.1%
116 60
 
< 0.1%
216 59
 
< 0.1%
765 58
 
< 0.1%
Other values (9420) 95919
78.7%
ValueCountFrequency (%)
0 2
< 0.1%
2 2
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
8 2
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 2
< 0.1%
12 4
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
12019.5 21760
17.9%
12018 1
 
< 0.1%
12015 1
 
< 0.1%
12013 2
 
< 0.1%
12012 1
 
< 0.1%
12010 1
 
< 0.1%
12007 1
 
< 0.1%
12000 2
 
< 0.1%
11999 1
 
< 0.1%
11993 1
 
< 0.1%

Registration_Days
Real number (ℝ)

Distinct13830
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4957.2564
Minimum0
Maximum15222
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:31.441552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile343
Q12102
median4493
Q37350
95-th percentile11316
Maximum15222
Range15222
Interquartile range (IQR)5248

Descriptive statistics

Standard deviation3450.8797
Coefficient of variation (CV)0.69612694
Kurtosis-0.35244164
Mean4957.2564
Median Absolute Deviation (MAD)2608
Skewness0.58609103
Sum6.0407144 × 108
Variance11908571
MonotonicityNot monotonic
2025-06-22T19:11:31.517485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4493 3651
 
3.0%
15222 380
 
0.3%
1 45
 
< 0.1%
4 38
 
< 0.1%
6 38
 
< 0.1%
9 38
 
< 0.1%
2 36
 
< 0.1%
0 35
 
< 0.1%
784 35
 
< 0.1%
511 35
 
< 0.1%
Other values (13820) 117525
96.4%
ValueCountFrequency (%)
0 35
< 0.1%
1 45
< 0.1%
2 36
< 0.1%
3 27
< 0.1%
4 38
< 0.1%
5 30
< 0.1%
6 38
< 0.1%
7 32
< 0.1%
8 31
< 0.1%
9 38
< 0.1%
ValueCountFrequency (%)
15222 380
0.3%
15218 1
 
< 0.1%
15217 1
 
< 0.1%
15214 2
 
< 0.1%
15209 2
 
< 0.1%
15191 1
 
< 0.1%
15190 1
 
< 0.1%
15187 1
 
< 0.1%
15186 2
 
< 0.1%
15182 1
 
< 0.1%

ID_Days
Real number (ℝ)

Distinct5962
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2999.9723
Minimum0
Maximum7197
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:31.589234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile385
Q11789
median3242
Q34263
95-th percentile4928
Maximum7197
Range7197
Interquartile range (IQR)2474

Descriptive statistics

Standard deviation1475.3142
Coefficient of variation (CV)0.49177596
Kurtosis-1.0089372
Mean2999.9723
Median Absolute Deviation (MAD)1143
Skewness-0.37740945
Sum3.6556462 × 108
Variance2176552.1
MonotonicityNot monotonic
2025-06-22T19:11:31.656838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3242 6015
 
4.9%
4053 76
 
0.1%
4032 74
 
0.1%
4375 73
 
0.1%
4312 70
 
0.1%
4144 69
 
0.1%
4250 67
 
0.1%
4619 66
 
0.1%
4193 64
 
0.1%
4404 64
 
0.1%
Other values (5952) 115218
94.6%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 22
< 0.1%
2 12
 
< 0.1%
3 23
< 0.1%
4 18
< 0.1%
5 20
< 0.1%
6 25
< 0.1%
7 43
< 0.1%
8 37
< 0.1%
9 27
< 0.1%
ValueCountFrequency (%)
7197 1
< 0.1%
6274 2
< 0.1%
6263 2
< 0.1%
6235 1
< 0.1%
6233 1
< 0.1%
6228 1
< 0.1%
6226 1
< 0.1%
6223 2
< 0.1%
6208 1
< 0.1%
6203 1
< 0.1%

Mobile_Tag
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
1
121856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 121856
100.0%

Length

2025-06-22T19:11:31.719469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:31.754011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 121856
100.0%

Most occurring characters

ValueCountFrequency (%)
1 121856
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 121856
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 121856
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 121856
100.0%

Homephone_Tag
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
121856 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 121856
100.0%

Length

2025-06-22T19:11:31.796423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:31.830384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 121856
100.0%

Most occurring characters

ValueCountFrequency (%)
0 121856
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 121856
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 121856
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 121856
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
87590 
1
34266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Length

2025-06-22T19:11:31.871000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:31.906783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 87590
71.9%
1 34266
 
28.1%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
Missing
41435 
Laborers
21024 
Sales
12136 
Core
10611 
Managers
8099 
Other values (14)
28551 

Length

Max length18
Median length15
Mean length7.4289571
Min length2

Characters and Unicode

Total characters905263
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowMissing
3rd rowRealty agents
4th rowMissing
5th rowLaborers

Common Values

ValueCountFrequency (%)
Missing 41435
34.0%
Laborers 21024
17.3%
Sales 12136
 
10.0%
Core 10611
 
8.7%
Managers 8099
 
6.6%
Drivers 7150
 
5.9%
High skill tech 4317
 
3.5%
Accountants 3766
 
3.1%
Medicine 3172
 
2.6%
Security 2683
 
2.2%
Other values (9) 7463
 
6.1%

Length

2025-06-22T19:11:31.955402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing 41435
31.3%
laborers 21811
16.5%
sales 12136
 
9.2%
core 10611
 
8.0%
managers 8099
 
6.1%
drivers 7150
 
5.4%
high 4317
 
3.3%
skill 4317
 
3.3%
tech 4317
 
3.3%
accountants 3766
 
2.8%
Other values (13) 14604
 
11.0%

Most occurring characters

ValueCountFrequency (%)
s 143264
15.8%
i 115468
12.8%
r 83411
9.2%
e 81066
9.0%
n 66697
 
7.4%
a 58752
 
6.5%
g 58077
 
6.4%
M 52706
 
5.8%
o 41423
 
4.6%
l 24346
 
2.7%
Other values (25) 180053
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 905263
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 143264
15.8%
i 115468
12.8%
r 83411
9.2%
e 81066
9.0%
n 66697
 
7.4%
a 58752
 
6.5%
g 58077
 
6.4%
M 52706
 
5.8%
o 41423
 
4.6%
l 24346
 
2.7%
Other values (25) 180053
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 905263
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 143264
15.8%
i 115468
12.8%
r 83411
9.2%
e 81066
9.0%
n 66697
 
7.4%
a 58752
 
6.5%
g 58077
 
6.4%
M 52706
 
5.8%
o 41423
 
4.6%
l 24346
 
2.7%
Other values (25) 180053
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 905263
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 143264
15.8%
i 115468
12.8%
r 83411
9.2%
e 81066
9.0%
n 66697
 
7.4%
a 58752
 
6.5%
g 58077
 
6.4%
M 52706
 
5.8%
o 41423
 
4.6%
l 24346
 
2.7%
Other values (25) 180053
19.9%

Client_Family_Members
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2.0
64062 
1.0
26213 
3.0
20434 
4.0
9583 
4.5
 
1564

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 64062
52.6%
1.0 26213
21.5%
3.0 20434
 
16.8%
4.0 9583
 
7.9%
4.5 1564
 
1.3%

Length

2025-06-22T19:11:32.013806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:32.059519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 64062
52.6%
1.0 26213
21.5%
3.0 20434
 
16.8%
4.0 9583
 
7.9%
4.5 1564
 
1.3%

Most occurring characters

ValueCountFrequency (%)
. 121856
33.3%
0 120292
32.9%
2 64062
17.5%
1 26213
 
7.2%
3 20434
 
5.6%
4 11147
 
3.0%
5 1564
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 121856
33.3%
0 120292
32.9%
2 64062
17.5%
1 26213
 
7.2%
3 20434
 
5.6%
4 11147
 
3.0%
5 1564
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 121856
33.3%
0 120292
32.9%
2 64062
17.5%
1 26213
 
7.2%
3 20434
 
5.6%
4 11147
 
3.0%
5 1564
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 121856
33.3%
0 120292
32.9%
2 64062
17.5%
1 26213
 
7.2%
3 20434
 
5.6%
4 11147
 
3.0%
5 1564
 
0.4%

Cleint_City_Rating
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2.0
121856 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters365568
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 121856
100.0%

Length

2025-06-22T19:11:32.114919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:32.150428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 121856
100.0%

Most occurring characters

ValueCountFrequency (%)
2 121856
33.3%
. 121856
33.3%
0 121856
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 121856
33.3%
. 121856
33.3%
0 121856
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 121856
33.3%
. 121856
33.3%
0 121856
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 365568
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 121856
33.3%
. 121856
33.3%
0 121856
33.3%

Application_Process_Day
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1565536
Minimum0
Maximum6
Zeros6287
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:32.177470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7415751
Coefficient of variation (CV)0.55173309
Kurtosis-1.0531977
Mean3.1565536
Median Absolute Deviation (MAD)1
Skewness0.013271482
Sum384645
Variance3.0330838
MonotonicityNot monotonic
2025-06-22T19:11:32.218986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 22544
18.5%
2 20907
17.2%
1 19712
16.2%
4 19668
16.1%
5 19613
16.1%
6 13125
10.8%
0 6287
 
5.2%
ValueCountFrequency (%)
0 6287
 
5.2%
1 19712
16.2%
2 20907
17.2%
3 22544
18.5%
4 19668
16.1%
5 19613
16.1%
6 13125
10.8%
ValueCountFrequency (%)
6 13125
10.8%
5 19613
16.1%
4 19668
16.1%
3 22544
18.5%
2 20907
17.2%
1 19712
16.2%
0 6287
 
5.2%

Application_Process_Hour
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.065947
Minimum4
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:32.269461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q110
median12
Q314
95-th percentile17
Maximum20
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2024775
Coefficient of variation (CV)0.26541452
Kurtosis-0.27212808
Mean12.065947
Median Absolute Deviation (MAD)2
Skewness-0.0025144516
Sum1470308
Variance10.255862
MonotonicityNot monotonic
2025-06-22T19:11:32.336887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
12 16640
13.7%
10 14465
11.9%
11 14413
11.8%
13 11765
9.7%
14 10702
8.8%
9 10525
8.6%
15 9614
7.9%
16 7739
6.4%
17 5843
 
4.8%
8 5821
 
4.8%
Other values (7) 14329
11.8%
ValueCountFrequency (%)
4 1526
 
1.3%
5 1437
 
1.2%
6 2247
 
1.8%
7 3441
 
2.8%
8 5821
 
4.8%
9 10525
8.6%
10 14465
11.9%
11 14413
11.8%
12 16640
13.7%
13 11765
9.7%
ValueCountFrequency (%)
20 739
 
0.6%
19 1464
 
1.2%
18 3475
 
2.9%
17 5843
 
4.8%
16 7739
6.4%
15 9614
7.9%
14 10702
8.8%
13 11765
9.7%
12 16640
13.7%
11 14413
11.8%

Client_Permanent_Match_Tag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
112454 
False
 
9402
ValueCountFrequency (%)
True 112454
92.3%
False 9402
 
7.7%
2025-06-22T19:11:32.388465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
100015 
False
21841 
ValueCountFrequency (%)
True 100015
82.1%
False 21841
 
17.9%
2025-06-22T19:11:32.420694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
2025-06-22T19:11:32.513265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length17
Mean length12.836775
Min length3

Characters and Unicode

Total characters1564238
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-employed
2nd rowGovernment
3rd rowSelf-employed
4th rowXNA
5th rowBusiness Entity Type 3
ValueCountFrequency (%)
type 50681
19.5%
business 36327
13.9%
entity 36327
13.9%
3 32962
12.7%
xna 21085
8.1%
self-employed 14725
 
5.7%
other 6290
 
2.4%
2 5826
 
2.2%
trade 5458
 
2.1%
industry 5431
 
2.1%
Other values (40) 45308
17.4%
2025-06-22T19:11:32.677717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 174946
 
11.2%
138564
 
8.9%
s 126333
 
8.1%
t 123312
 
7.9%
y 111367
 
7.1%
n 109691
 
7.0%
i 99237
 
6.3%
p 68871
 
4.4%
u 50792
 
3.2%
T 45475
 
2.9%
Other values (42) 515650
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1564238
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 174946
 
11.2%
138564
 
8.9%
s 126333
 
8.1%
t 123312
 
7.9%
y 111367
 
7.1%
n 109691
 
7.0%
i 99237
 
6.3%
p 68871
 
4.4%
u 50792
 
3.2%
T 45475
 
2.9%
Other values (42) 515650
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1564238
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 174946
 
11.2%
138564
 
8.9%
s 126333
 
8.1%
t 123312
 
7.9%
y 111367
 
7.1%
n 109691
 
7.0%
i 99237
 
6.3%
p 68871
 
4.4%
u 50792
 
3.2%
T 45475
 
2.9%
Other values (42) 515650
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1564238
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 174946
 
11.2%
138564
 
8.9%
s 126333
 
8.1%
t 123312
 
7.9%
y 111367
 
7.1%
n 109691
 
7.0%
i 99237
 
6.3%
p 68871
 
4.4%
u 50792
 
3.2%
T 45475
 
2.9%
Other values (42) 515650
33.0%

Score_Source_2
Real number (ℝ)

Distinct66473
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51595656
Minimum0.015246378
Maximum1.0465776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:32.740025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.015246378
5-th percentile0.1370556
Q10.4019956
median0.56497814
Q30.65982842
95-th percentile0.74646571
Maximum1.0465776
Range1.0313313
Interquartile range (IQR)0.25783282

Descriptive statistics

Standard deviation0.1875338
Coefficient of variation (CV)0.3634682
Kurtosis-0.16125873
Mean0.51595656
Median Absolute Deviation (MAD)0.11418797
Skewness-0.82783849
Sum62872.402
Variance0.035168927
MonotonicityNot monotonic
2025-06-22T19:11:32.818151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.564978137 5689
 
4.7%
0.01524637788 618
 
0.5%
0.285897872 269
 
0.2%
0.262258369 137
 
0.1%
0.159679234 135
 
0.1%
0.26525634 124
 
0.1%
0.265311748 116
 
0.1%
0.263143591 99
 
0.1%
0.162192106 87
 
0.1%
0.162144568 86
 
0.1%
Other values (66463) 114496
94.0%
ValueCountFrequency (%)
0.01524637788 618
0.5%
0.015284636 1
 
< 0.1%
0.015308351 1
 
< 0.1%
0.015314616 1
 
< 0.1%
0.015325832 1
 
< 0.1%
0.015334414 1
 
< 0.1%
0.015343332 1
 
< 0.1%
0.015426144 1
 
< 0.1%
0.015431791 1
 
< 0.1%
0.015460058 1
 
< 0.1%
ValueCountFrequency (%)
1.046577639 6
< 0.1%
0.854999666 8
< 0.1%
0.821393627 1
 
< 0.1%
0.820609506 1
 
< 0.1%
0.820487147 1
 
< 0.1%
0.818575745 1
 
< 0.1%
0.818403562 1
 
< 0.1%
0.817367791 1
 
< 0.1%
0.815647763 1
 
< 0.1%
0.815601725 1
 
< 0.1%

Score_Source_3
Real number (ℝ)

Distinct639
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51601249
Minimum0.065164104
Maximum0.89600955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:32.888535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.065164104
5-th percentile0.17876047
Q10.40487836
median0.5460232
Q30.63135454
95-th percentile0.77389569
Maximum0.89600955
Range0.83084544
Interquartile range (IQR)0.22647617

Descriptive statistics

Standard deviation0.17232248
Coefficient of variation (CV)0.33395021
Kurtosis-0.10374957
Mean0.51601249
Median Absolute Deviation (MAD)0.10850608
Skewness-0.51747703
Sum62879.218
Variance0.029695036
MonotonicityNot monotonic
2025-06-22T19:11:32.955119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.546023197 24845
 
20.4%
0.385914672 2592
 
2.1%
0.0651641045 979
 
0.8%
0.746300213 567
 
0.5%
0.694092643 526
 
0.4%
0.7136314 509
 
0.4%
0.554946769 474
 
0.4%
0.670651753 470
 
0.4%
0.6577838 454
 
0.4%
0.595456203 438
 
0.4%
Other values (629) 90002
73.9%
ValueCountFrequency (%)
0.0651641045 979
0.8%
0.065550026 11
 
< 0.1%
0.065993207 9
 
< 0.1%
0.066439172 13
 
< 0.1%
0.066887934 10
 
< 0.1%
0.067339509 8
 
< 0.1%
0.067793911 11
 
< 0.1%
0.068251154 14
 
< 0.1%
0.068711255 6
 
< 0.1%
0.069174227 8
 
< 0.1%
ValueCountFrequency (%)
0.896009549 1
 
< 0.1%
0.893976075 2
 
< 0.1%
0.885488394 3
 
< 0.1%
0.882530313 13
< 0.1%
0.881026575 4
 
< 0.1%
0.88026848 18
< 0.1%
0.879506216 1
 
< 0.1%
0.878739767 4
 
< 0.1%
0.877194258 3
 
< 0.1%
0.874844253 1
 
< 0.1%

Phone_Change
Real number (ℝ)

Zeros 

Distinct3422
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.46462
Minimum0
Maximum3444.5
Zeros14555
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:33.024638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1287
median755
Q31550
95-th percentile2510
Maximum3444.5
Range3444.5
Interquartile range (IQR)1263

Descriptive statistics

Standard deviation814.84724
Coefficient of variation (CV)0.85282827
Kurtosis-0.26019921
Mean955.46462
Median Absolute Deviation (MAD)602
Skewness0.73681343
Sum1.164291 × 108
Variance663976.02
MonotonicityNot monotonic
2025-06-22T19:11:33.097711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14555
 
11.9%
755 3697
 
3.0%
1 1104
 
0.9%
2 916
 
0.8%
3 645
 
0.5%
4 524
 
0.4%
5 322
 
0.3%
3444.5 260
 
0.2%
6 216
 
0.2%
7 179
 
0.1%
Other values (3412) 99438
81.6%
ValueCountFrequency (%)
0 14555
11.9%
1 1104
 
0.9%
2 916
 
0.8%
3 645
 
0.5%
4 524
 
0.4%
5 322
 
0.3%
6 216
 
0.2%
7 179
 
0.1%
8 118
 
0.1%
9 72
 
0.1%
ValueCountFrequency (%)
3444.5 260
0.2%
3444 2
 
< 0.1%
3442 1
 
< 0.1%
3441 1
 
< 0.1%
3440 3
 
< 0.1%
3439 2
 
< 0.1%
3438 2
 
< 0.1%
3437 2
 
< 0.1%
3436 3
 
< 0.1%
3435 3
 
< 0.1%

Credit_Bureau
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7317654
Minimum0
Maximum6
Zeros28003
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-06-22T19:11:33.152350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6147541
Coefficient of variation (CV)0.93243238
Kurtosis0.41451571
Mean1.7317654
Median Absolute Deviation (MAD)1
Skewness1.0531964
Sum211026
Variance2.6074308
MonotonicityNot monotonic
2025-06-22T19:11:33.195154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 41287
33.9%
0 28003
23.0%
2 21431
17.6%
3 13102
 
10.8%
4 7978
 
6.5%
6 5384
 
4.4%
5 4671
 
3.8%
ValueCountFrequency (%)
0 28003
23.0%
1 41287
33.9%
2 21431
17.6%
3 13102
 
10.8%
4 7978
 
6.5%
5 4671
 
3.8%
6 5384
 
4.4%
ValueCountFrequency (%)
6 5384
 
4.4%
5 4671
 
3.8%
4 7978
 
6.5%
3 13102
 
10.8%
2 21431
17.6%
1 41287
33.9%
0 28003
23.0%

Default
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
0
112011 
1
 
9845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Length

2025-06-22T19:11:33.248664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-22T19:11:33.280974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112011
91.9%
1 9845
 
8.1%

Interactions

2025-06-22T19:11:26.470284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:07.971582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.300462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.583143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.629266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.656563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.055241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.094524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.128096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.137384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.166086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.563314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.523576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.445035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.547261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.077132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.390323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.659533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.703481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.728013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.132250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.167580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.199514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.207093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.239619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.630342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.589547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.520982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.621077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.171992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.497832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.736058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.779874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.806442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.206955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.246448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.275381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.283404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.311348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.701250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.657324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.597741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.689789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.252867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.590099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.811237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.851218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.889530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.286635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.327172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.352280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.357331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.396674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.769092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.722580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.667381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.757368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.342300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.692282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.884683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.922529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.975831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.363999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.398488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.421868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.422440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.470132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.837959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.785326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.738673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.828479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.425000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.793073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.958163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.998862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.054216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.441242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.473081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.493686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.500390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.540468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.907492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.852569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.810832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.897568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.501444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.895525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.044221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.072008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.126754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.513373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.543166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.566052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.569555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.622777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.975535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.921134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.884809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.969958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:08.587054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.999192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.122141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.145431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.208076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.594614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.617706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.637112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.642026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.697321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.050266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.987711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.958916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:27.044128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:12.763608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.089808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.198571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.222395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:11:18.666690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.689241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.707125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.710649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.772035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.120898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.060097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.035546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:27.113861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:12.861144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.177097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.270272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.294759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.356084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.737118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.755983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.780993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.780339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.196680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:11:12.958096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.261034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.346836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:11:19.836349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.856203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.852313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.273629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.255458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.193352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.187318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:27.278646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.055065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.348353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.416306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.441018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.503597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.881724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.901049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.926882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.921659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.344843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.320446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.253091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.259715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:27.342470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.138793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:11:16.508970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.913955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:18.946218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.975317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.993894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-06-22T19:11:23.417422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.382642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.315389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.323836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:27.410247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:13.222211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:14.502973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:15.558984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:16.582690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:17.986732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:19.019870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:20.056784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:21.067939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:22.091161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:23.493488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:24.455946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:25.379342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-22T19:11:26.397847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-22T19:11:33.342817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Accompany_ClientActive_LoanAge_DaysApplication_Process_DayApplication_Process_HourBike_OwnedCar_OwnedChild_CountClient_Contact_Work_TagClient_EducationClient_Family_MembersClient_GenderClient_Housing_TypeClient_IncomeClient_Income_TypeClient_Marital_StatusClient_OccupationClient_Permanent_Match_TagCredit_AmountCredit_BureauDefaultEmployed_DaysHouse_OwnID_DaysLoan_AnnuityLoan_Contract_TypePhone_ChangePopulation_Region_RelativeRegistration_DaysScore_Source_2Score_Source_3Workphone_Working
Accompany_Client1.0000.0000.0250.0180.0190.0030.0400.0220.0160.0240.0560.0460.0160.0190.0190.0740.0210.0220.0160.0120.0090.0220.0550.0070.0160.0150.0140.0230.0130.0080.0190.014
Active_Loan0.0001.0000.0090.0060.0040.0000.0000.0000.0030.0000.0040.0040.0060.0000.0000.0000.0020.0050.0000.0070.0000.0000.0040.0000.0000.0000.0120.0100.0000.0000.0000.000
Age_Days0.0250.0091.0000.010-0.0900.0000.1540.2620.1780.1120.2320.1040.120-0.0810.2740.2190.1700.1860.0630.0540.0730.5540.1200.244-0.0090.1210.0720.0380.2830.0980.1760.044
Application_Process_Day0.0180.0060.0101.000-0.0240.0000.0000.0120.0050.0000.0100.0080.007-0.0050.0160.0040.0190.006-0.0040.0090.0060.0010.0230.003-0.0000.021-0.003-0.0050.001-0.0000.0000.013
Application_Process_Hour0.0190.004-0.090-0.0241.0000.0050.0160.0160.0270.0490.0160.0120.0190.0990.0470.0280.0330.0190.049-0.0320.026-0.0740.124-0.0320.0540.0360.0030.1220.0130.156-0.0290.074
Bike_Owned0.0030.0000.0000.0000.0051.0000.0000.0020.0020.0030.0020.0050.0000.0000.0000.0030.0090.0000.0020.0030.0000.0050.0000.0080.0000.0050.0050.0000.0000.0080.0000.005
Car_Owned0.0400.0000.1540.0000.0160.0001.0000.1010.0890.0900.1540.3370.0380.2130.1540.1520.2450.0000.1120.0340.0220.1570.0070.0220.1370.0000.0360.0460.0910.0510.0270.008
Child_Count0.0220.0000.2620.0120.0160.0020.1011.0000.0680.0310.9050.0330.0260.0290.1510.1120.1110.0290.0130.0230.0220.1590.0130.0380.0230.0370.0130.0250.1110.0130.0250.035
Client_Contact_Work_Tag0.0160.0030.1780.0050.0270.0020.0890.0681.0000.0330.0750.1310.0380.0460.2160.0650.1800.0250.0100.0230.0280.2240.0330.0680.0290.0060.0180.0430.0790.0660.0410.021
Client_Education0.0240.0000.1120.0000.0490.0030.0900.0310.0331.0000.0300.0190.0400.1240.1000.0530.1790.0340.0680.0360.0620.0720.0280.0400.0770.0620.0200.0530.0530.0660.0250.031
Client_Family_Members0.0560.0040.2320.0100.0160.0020.1540.9050.0750.0301.0000.0580.0460.0260.1270.5000.0950.0310.0600.0190.0210.1360.0170.0330.0590.0400.0240.0200.0950.0170.0200.041
Client_Gender0.0460.0040.1040.0080.0120.0050.3370.0330.1310.0190.0581.0000.0480.1470.1160.1110.3470.0480.0210.0190.0500.1250.0420.0300.0560.0170.0240.0110.0640.0120.0160.023
Client_Housing_Type0.0160.0060.1200.0070.0190.0000.0380.0260.0380.0400.0460.0481.0000.0180.0520.0840.0410.1900.0280.0120.0340.0670.2150.0440.0180.0250.0210.0550.0490.0250.0300.034
Client_Income0.0190.000-0.081-0.0050.0990.0000.2130.0290.0460.1240.0260.1470.0181.0000.1040.0460.1050.0290.3990.0630.029-0.0850.012-0.0330.4630.0530.0590.089-0.0680.167-0.0740.041
Client_Income_Type0.0190.0000.2740.0160.0470.0000.1540.1510.2160.1000.1270.1160.0520.1041.0000.1410.2670.0900.0470.0210.0580.3560.0660.1100.0620.0580.0210.0780.0980.0380.0410.019
Client_Marital_Status0.0740.0000.2190.0040.0280.0030.1520.1120.0650.0530.5000.1110.0840.0460.1411.0000.1040.0580.0760.0210.0260.1510.0470.0720.0750.0440.0390.0160.0820.0230.0350.024
Client_Occupation0.0210.0020.1700.0190.0330.0090.2450.1110.1800.1790.0950.3470.0410.1050.2670.1041.0000.0740.0510.0290.0760.2140.0450.0670.0620.0540.0200.0300.0630.0390.0270.044
Client_Permanent_Match_Tag0.0220.0050.1860.0060.0190.0000.0000.0290.0250.0340.0310.0480.1900.0290.0900.0580.0741.0000.0320.0140.0420.1410.0540.0790.0100.0150.0580.0450.0870.0400.0710.049
Credit_Amount0.0160.0000.063-0.0040.0490.0020.1120.0130.0100.0680.0600.0210.0280.3990.0470.0760.0510.0321.000-0.0250.0560.0250.0660.0060.7990.2770.0730.052-0.0040.1200.0210.045
Credit_Bureau0.0120.0070.0540.009-0.0320.0030.0340.0230.0230.0360.0190.0190.0120.0630.0210.0210.0290.014-0.0251.0000.1480.0370.0670.0340.0030.0620.1460.0040.015-0.025-0.0650.026
Default0.0090.0000.0730.0060.0260.0000.0220.0220.0280.0620.0210.0500.0340.0290.0580.0260.0760.0420.0560.1481.0000.0870.0000.0530.0400.0270.0550.0410.0380.1550.3000.025
Employed_Days0.0220.0000.5540.001-0.0740.0050.1570.1590.2240.0720.1360.1250.067-0.0850.3560.1510.2140.1410.0250.0370.0871.0000.0730.251-0.0290.0630.0940.0030.1870.0610.1510.052
House_Own0.0550.0040.1200.0230.1240.0000.0070.0130.0330.0280.0170.0420.2150.0120.0660.0470.0450.0540.0660.0670.0000.0731.0000.0340.0310.0650.0490.0590.0470.0060.0310.040
ID_Days0.0070.0000.2440.003-0.0320.0080.0220.0380.0680.0400.0330.0300.044-0.0330.1100.0720.0670.0790.0060.0340.0530.2510.0341.000-0.0160.0510.0770.0150.0930.0500.1210.036
Loan_Annuity0.0160.000-0.009-0.0000.0540.0000.1370.0230.0290.0770.0590.0560.0180.4630.0620.0750.0620.0100.7990.0030.040-0.0290.031-0.0161.0000.4110.0620.051-0.0340.1170.0100.023
Loan_Contract_Type0.0150.0000.1210.0210.0360.0050.0000.0370.0060.0620.0400.0170.0250.0530.0580.0440.0540.0150.2770.0620.0270.0630.0650.0510.4111.0000.0710.0290.0240.0220.0190.021
Phone_Change0.0140.0120.072-0.0030.0030.0050.0360.0130.0180.0200.0240.0240.0210.0590.0210.0390.0200.0580.0730.1460.0550.0940.0490.0770.0620.0711.0000.0330.0520.1980.0510.071
Population_Region_Relative0.0230.0100.038-0.0050.1220.0000.0460.0250.0430.0530.0200.0110.0550.0890.0780.0160.0300.0450.0520.0040.0410.0030.0590.0150.0510.0290.0331.0000.0330.1790.0040.079
Registration_Days0.0130.0000.2830.0010.0130.0000.0910.1110.0790.0530.0950.0640.049-0.0680.0980.0820.0630.087-0.0040.0150.0380.1870.0470.093-0.0340.0240.0520.0331.0000.0630.0930.075
Score_Source_20.0080.0000.098-0.0000.1560.0080.0510.0130.0660.0660.0170.0120.0250.1670.0380.0230.0390.0400.120-0.0250.1550.0610.0060.0500.1170.0220.1980.1790.0631.0000.0990.069
Score_Source_30.0190.0000.1760.000-0.0290.0000.0270.0250.0410.0250.0200.0160.030-0.0740.0410.0350.0270.0710.021-0.0650.3000.1510.0310.1210.0100.0190.0510.0040.0930.0991.0000.017
Workphone_Working0.0140.0000.0440.0130.0740.0050.0080.0350.0210.0310.0410.0230.0340.0410.0190.0240.0440.0490.0450.0260.0250.0520.0400.0360.0230.0210.0710.0790.0750.0690.0171.000

Missing values

2025-06-22T19:11:27.588885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-22T19:11:28.070837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Client_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_2Score_Source_3Phone_ChangeCredit_BureauDefault
06750.000.000.001.000.000.0061190.553416.85AloneCommercialSecondaryMMaleCLHome0.0313957.001062.006123.00383.00100Sales2.002.006.0017.00YesYesSelf-employed0.480.5563.001.000
120250.001.000.001.001.000.0015282.001826.55AloneServiceGraduationMMaleCLHome0.0114162.004129.007833.0021.00101Missing2.002.003.0010.00YesYesGovernment0.220.55755.001.000
218000.000.000.001.000.001.0059527.352788.20AloneServiceGraduation dropoutWMaleCLFamily0.0216790.005102.004493.00331.00100Realty agents2.002.004.0012.00YesYesSelf-employed0.550.33277.000.000
315750.000.000.001.001.000.0053870.402295.45AloneRetiredSecondaryMMaleCLHome0.0123195.0012019.504493.00775.00100Missing2.002.002.0015.00YesYesXNA0.140.631700.003.000
433750.001.000.001.000.002.00133988.403547.35AloneCommercialSecondaryMFemaleCLHome0.0211366.002977.005516.004043.00100Laborers4.002.003.0012.00YesYesBusiness Entity Type 30.300.36674.001.000
511250.000.001.001.001.001.0013752.00653.85AloneServiceSecondaryWFemaleCLHome0.0213881.001184.003910.003910.00100Laborers2.002.002.0010.00YesYesOther0.700.42739.000.000
615750.001.001.000.001.000.00128835.003779.55AloneRetiredSecondarySMaleCLHome0.0221323.0012019.50113.004855.00100Missing1.002.003.0014.00YesYesXNA0.600.510.003.000
713500.000.000.001.001.000.0060415.203097.80AloneRetiredSecondaryMMaleCLHome0.0122493.0012019.5012617.005280.00101Missing2.002.004.0015.00YesYesXNA0.660.551687.004.000
813500.001.001.000.001.001.0045000.001200.15RelativeCommercialGraduationMFemaleCLHome0.0115734.007889.005455.002665.00101Sales3.002.004.0013.00YesYesSelf-employed0.640.551611.000.000
912150.000.000.000.001.000.0016320.151294.65AloneRetiredSecondaryWMaleCLHome0.0220507.0012019.502834.004053.00100Missing1.002.003.009.00YesYesXNA0.060.08533.005.000
Client_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_2Score_Source_3Phone_ChangeCredit_BureauDefault
12184612150.000.001.000.001.000.0025470.001462.05AloneRetiredGraduation dropoutSMaleCLHome0.0324123.0012019.509523.00795.00100Missing1.002.003.009.00YesYesXNA0.560.550.000.000
12184715750.001.000.001.001.000.0026128.801283.85AloneCommercialSecondaryMMaleCLHome0.0214025.001107.00507.004514.00100Managers2.002.005.009.00YesYesBusiness Entity Type 30.730.441175.001.000
12184818000.001.001.000.000.001.0027302.402169.90AloneServiceSecondaryMFemaleCLHome0.0411073.001521.004883.003602.00100Sales3.002.002.0014.00YesYesHousing0.630.311718.002.000
12184910350.000.001.000.000.000.0018792.901736.55AloneServiceGraduation dropoutSMaleCLMunicipal0.019204.00763.003773.001874.00101Sales1.002.003.0011.00YesYesSelf-employed0.620.55774.001.000
12185012150.000.000.001.000.000.0078192.002383.65AloneRetiredSecondarySMaleCLHome0.0223943.0012019.501213.004011.00100Missing1.002.002.0011.00YesYesXNA0.680.281581.002.000
12185129250.000.000.000.001.000.00107820.003165.30RelativeServiceSecondaryMFemaleCLHome0.0312889.002863.002661.002943.00100Laborers2.002.004.0016.00YesNoBusiness Entity Type 20.170.180.001.001
12185215750.000.001.001.000.000.00104256.003388.05AloneCommercialGraduationMFemaleCLHome0.028648.00636.00902.001209.00100Sales2.002.004.0012.00YesYesSelf-employed0.370.414.000.000
1218538100.000.001.000.001.001.0055107.902989.35AloneGovt JobSecondaryMMaleCLHome0.019152.001623.003980.00353.00100High skill tech3.002.005.0011.00NoNoTrade: type 60.050.550.001.000
12185433750.001.001.000.001.000.0045000.002719.35AloneServiceGraduationMFemaleCLHome0.0310290.00847.00895.002902.00100Sales2.002.001.0012.00YesYesBusiness Entity Type 30.100.080.002.000
1218559000.001.001.001.001.001.0062428.954201.65AloneCommercialSecondarySMaleCLHome0.0214772.00498.008679.005025.00100Managers2.002.004.006.00YesYesBusiness Entity Type 30.560.30805.000.000

Duplicate rows

Most frequently occurring

Client_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_2Score_Source_3Phone_ChangeCredit_BureauDefault# duplicates
02700.000.000.001.001.000.009594.001020.15AloneServiceSecondaryMMaleCLHome0.0319128.001323.004646.002670.00100Security2.002.005.007.00YesNoAgriculture0.340.693.001.0002
12700.000.000.001.001.000.0022752.00870.75AloneRetiredSecondaryDMaleCLHome0.0122313.0012019.50330.004801.00100Missing1.002.002.0010.00YesYesXNA0.230.612343.000.0002
22700.001.001.001.000.000.0076022.553233.70AloneRetiredSecondaryMMaleCLHome0.0220923.0012019.502281.004289.00100Missing2.002.001.0011.00YesYesXNA0.680.64621.002.0002
33015.000.001.000.001.000.0028856.251669.50AloneRetiredJunior secondaryMFemaleCLHome0.0323770.0012019.501053.004945.00100Missing2.002.004.007.00YesYesXNA0.160.75210.002.0002
43150.000.000.000.000.002.0010188.00676.35AloneCommercialSecondarySMaleCLMunicipal0.0211240.00776.0010403.003470.00100Low-skill Laborers3.002.002.009.00YesNoBusiness Entity Type 20.180.55448.001.0002
53150.000.000.001.000.002.0033750.002666.25AloneCommercialSecondaryMMaleCLHome0.0314280.00179.007385.003102.00100Cleaning4.002.004.0012.00YesYesSchool0.780.551810.001.0002
63150.000.000.001.001.000.0015638.401615.50PartnerRetiredSecondaryMMaleCLHome0.0123766.0012019.504919.004931.00101Missing2.002.001.0012.00YesYesXNA0.620.680.001.0002
73150.000.000.001.001.000.0018000.00900.00RelativeServiceSecondaryMMaleRLHome0.0317648.00489.004332.001196.00100Core2.002.001.0011.00NoYesGovernment0.790.551495.000.0002
83240.000.000.000.001.000.0011376.00648.00KidsServiceSecondaryMMaleCLHome0.0218392.001581.006643.001945.00100Missing2.002.003.0012.00YesYesBusiness Entity Type 30.670.791047.001.0002
93375.000.001.000.000.000.00107820.003165.30AloneRetiredSecondaryMMaleCLHome0.0521157.0012019.506859.004247.00100Missing2.002.002.0017.00YesYesXNA0.510.50785.002.0002